At present, with the booming of mobile broadband technologies in communication networks, the network traffic presents an explosive growth. In order to meet higher and higher requirements that people have for mobile communications in the future, to get rid of the constraint of the site and environments and to realize ubiquity of networks in a real sense, how a communication network provides high speed data services to a user and supports the user to implement seamless roaming services in various types of wireless communication systems, and especially provides heat compensation and blind compensation control in traffic hotspots and areas having poor coverage appear to be particularly important.
It is very difficult for the spectrum under the existing network deployment mainly based on macro base stations to meet the capacity requirements. For this purpose, it would be a better solution at present to introduce small base stations into a communication network to realize common-frequency networking of a multi-layer heterogeneous network, and at the same time this also brings the fusion and development of multiple types of wireless communication networks. A network like this which does not have the same transmission properties and communication protocols is called a wireless heterogeneous network (Heterogeneous Net). On one hand, the Heterogeneous Net effectively reduces the operation and maintenance costs of the operator; on the other hand, the service experience of a terminal user is also apparently improved.
In a current wireless communication network, no matter it is a traditional homogeneous network or a multi-network converged heterogeneous network, the target thereof is to provide a larger system capacity and better quality of service for mobile users. In order to effectively reduce interferences to a system, improve the system capacity and ensure the quality of a communication link, it is required to perform reasonable dynamic allocation on limited wireless resources such as frequency bands, transmission power and channels, so that the resources of the system can be sufficiently utilized and the performance of the system can be optimized, thereby realizing the above-mentioned target.
Among the numerous key wireless technologies, power control is a difficulty and is also one of the key points at the same time. The main purpose of power control is to suppress interferences by means of controlling the transmission power of a terminal or a base station. The basic principle is to dynamically adjust the transmission power of a transmitter in real time and enable the received power at a receiver to be as small as possible on the premise that normal communication quality requirements are satisfied. If all the transmitters in the system use the minimum transmitting power which just meets this requirement, then the interference in the system will be greatly reduced without reducing the receiving quality of a single receiver. When the decreased amplitude of the interference power is greater that the decreased amplitude of the transmitting power, the capacity of the entire system can be increased. In addition, power control also functions to save energy in the communications. For a user of a mobile terminal, if a normal communication with a base station can be established using a transmission power as small as possible, the discharging time of a battery can certainly be prolonged. Therefore, a reasonable and effective power control solution will enable the entire system to present characteristics of high capacity and high quality of service.
Power control is generally divided into uplink power control and downlink power control, and the uplink power control and the downlink power control are performed independently. Traditional power control methods may typically be divided into two types as follows: one is a power control method based on optimization and the other one is a power control method based on feedback.
With regard to a power control algorithm based on optimization, an accurate model of the entire system needs to be known, and the system performance is described by means of a target function. The calculation of the transmission power of each mobile terminal is completed under the target of achieving an optimal system performance, and then the calculated power value is loaded to the mobile terminal in real time. This method has accurate control and has an explicitly meaningful optimized target function, however, the calculation amount thereof is huge, and the method is not suitable for dynamic environments. When environment parameters change, an optimal solution obtained previously does not work any more, especially when the dimension of the system and the number of users change, a system model needs to be re-established and optimized once again. In this case, the real-time completion of a task is difficult to implement, and therefore the power control algorithm based on optimization has only theoretic study value rather than any practical significance.
In addition, the power control algorithm based on feedback has flexible control and is easy to implement. However, the determination of a step-length is largely depended on experience knowledge, and the algorithm lacks theoretical foundation. If an inappropriate step-length is chosen, it will give rise to large over adjustment or a long stabilization time, thereby influencing the signal to interference ratio of each user and the system stability. Moreover, this power control algorithm based on feedback determines the trend of change of the transmission power according to the change of quality of service for a single user, without involving the concept of the overall optimization of the system, and thus it is very difficult to make the system to arrive at the overall optimization state.
At present, typical power control algorithms are generally divided into the following types: a traditional power control algorithm with a fixed step-length, a power control algorithm based on the measurement of received signal strength, a power control algorithm based on the transmission quality of a communication link (such as SIR (Signal to Interference Ratio) and BER (Bit Error Rate)) and a power control algorithm based on random theories, etc., which will be briefly described below respectively.
(I) A Traditional Power Control Algorithm with a Fixed Step-Length
Take the uplink as an example, if the transmission power of a mobile station is set to P(t) which is adjusted by a step-length Δp in each power control period Tp:P(t)=P(t−Tp)±Δp; 
where step-length Δp is fixed to 1 dB.
This power control algorithm allows a base station to send a power control command, and a user adjusts a transmitting power with a fixed step-length according to this control command. The power changing process is just link “ping-pong” control, as a result, this power control algorithm has a poor stability, and cases of excessive over adjustment or too short stabilization time may easily occur.
(II) A Power Control Algorithm Based on the Measurement of Received Signal Strength
The acquisition of the estimation of signal strength is relatively easy for most of mobile communication systems, and thus most of the algorithms are performed around the measurement based on the received signal strength. Likewise, in the algorithm, with regard to the uplink, the received signal strength of a receiver of a mobile station is Ci(t), which has a linear relationship with the transmission power of the mobile station Pi(t), then this static control algorithm based on the measurement of the received signal strength is expressed using the following formula:Pi(t+1)=α+βCi(t);
where β is a constant greater than zero; and in the algorithm, the selection of its parameters α, β has vital influences on the system performance.
(III) A power control algorithm based on the transmission quality of a communication link
The transmission quality of the communication link can be measured using the signal-to-noise ratio (SIR) or bit error ratio (BER). Now a centralized power control algorithm based on SIR balance is taken as an example to introduce such algorithm.
For a multi-cell multi-user cellular system, the number of cell base stations in the system is set to N, and each cell has Mn (n=1, 2, 3 . . . N) mobile users. The uplink is taken as an example, if the signal-interference ratio of a mobile user i belonging to the kth cell is denoted by Γi(t), thus:
            Γ      i        =                                                      G              ki                        ⁢                          P              i                                                                          ∑                                  j                  ≠                  i                                            ⁢                                                G                  kj                                ⁢                                  P                  j                                                      +                          η              i                                      ⁢                                  ⁢        k            =      1        ,  2  ,            3      ⁢                          ⁢      …      ⁢                          ⁢      N        ;  
where Gki a link gain from the mobile user i to base station k, and Pi is the transmission power of the user i. The threshold value of SIR required under the condition of assurance of the quality of the communication link is set to γi, then to ensure the communication quality, there should be:Γi≧Γi≧γi;
and if the impact of noise is not taken into consideration, then:
            Γ      i        =                                        G            ki                    ⁢                      P            i                                                ∑                          j              ≠              i                                ⁢                                    G              kj                        ⁢                          P              j                                          =                        P          i                                      ∑                          j              ≠              i                                ⁢                                    Z              kj                        ⁢                          P              j                                            ;
where
      Z    kj    =      {                                                      G              kj                                      G              ki                                                            j            ≠            k                                                0                                      j            =            k                              is the normalized link gain matrix, the following is obtained after substituting Zkj in the above formula:Pi≧(γi)(ΣPjZij);
written in the matrix form:
            P      ⁢              1        γ              ≥    PZ    ;
where Z=Zkj; P=(P1, P2, P3, . . . PN)T, P>0; γ=γ1, γ2, γ3, . . . γN)T. The power control algorithm based on SIR balance determines the assurance transmission power vector P utilizing some measurement information. Since gains of all the links need to be known in the process of resolving the power P, this algorithm is a centralized power control algorithm. The centralized power control algorithm leads to good control performances, and can be considered as the optimal power control. However, the centralized power control algorithm has one drawback, i.e.: the calculation amount for obtaining a normalized link matrix at a certain moment is relatively large.
(IV) A Power Control Algorithm Based on Random Theories
Ulukus and Yates propose an extended power control algorithm based on random theories, which algorithm is expressed with the following formula:
                    P        i            ⁡              (                  t          +          1                )              =                            [                      1            -                                          α                i                            ⁡                              (                                  1                  +                                      γ                    i                                                  )                                              ]                ⁢                              P            i                    ⁡                      (            t            )                              +                        α          i                ⁢                              v            i                    ⁡                      (            t            )                          ⁢                              γ            i                                G            ik                                ;
where Gik is the channel gain between a mobile station i and a base station k which establishes a connection with the mobile station i; αn, n=1, 2, 3 . . . . , satisfies the condition αi=ε or
            α      i        =          ɛ      t        ,ε being a small positive constant; vi(t) is an SMF (Squared Matched Filter) output of the mobile station i at a moment t, and it is a random noise with Gauss distribution which has one mean value of zero and variance of σ2.
In addition to the four types of basic power control algorithms generalized above, branch researches of many other power control algorithms are not excluded. On the whole, most of the power control algorithms are improvement implemented on the basis of the traditional power control algorithms. On the basis that various types of algorithms have different emphases, some are embodied in the aspect of hardware feasibility, some focus on the improvement of the overall network performance, and some mainly focus on the simplification of the model to realize simple calculation, etc. On the one hand, these traditional power control algorithms have certain reliability and stability after a long practice process; on the other hand, they are confirmed to show certain limitations and complexity in a long term evolution communication network.